The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A novel framework for SVM-based image retrieval on large databases
Proceedings of the 13th annual ACM international conference on Multimedia
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Descriptor learning for efficient retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Learning similarity measure for natural image retrieval with relevance feedback
IEEE Transactions on Neural Networks
Exploiting visual word co-occurrence for image retrieval
Proceedings of the 20th ACM international conference on Multimedia
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Recently, the Bag-of-Feature (BoF) model has shown promising performance in object and generic image retrieval. The similarity between two images is typically measured by the distance between the two histograms. Due to the imperfection of local descriptor and quantization error, visually similar image patches can be wrongly quantized into different visual words, making this distance-based measure less accurate. To address this issue, this paper explores the information of latent class, which is formed by all the database images that share the same visual concept with the one being compared to a given query. We then cast image similarity as the probability of the query and a database image belonging to a same latent class. Considering that a class of images together can better depict a visual concept, the shift from image-to-image to image-to-class comparison is expected to bring a more robust similarity measure. Because the ground truth of the latent class is not accessible in image retrieval, we define a latent class prior in our probabilistic model and derive its marginal distribution. This gives rise to a novel and efficient image similarity measure. It can significantly improve retrieval performance without prolonging retrieval process. Experimental study on multiple benchmark data sets demonstrates its advantages.